Method for detecting the impacts of interfering effects on experimental data

ABSTRACT

The invention provides a method for identifying the impacts of interfering effects on experimental data. In particular, a method is described for identifying the impacts of unwanted auto-fluorescence, fluorescence quenching, and deterioration of a fluorescent sample under study on the collected experimental data. The data are analyzed whether or not said data fulfill certain criteria with respect to a threshold which is indicative for said interfering effect.

This invention relates to a method for detecting the impacts ofinterfering effects on experimental data such as secondary lightemission data. More particularly, the invention relates to a method fordetecting impacts of the effects of unwanted auto-fluorescence, offluorescence quenching, and/or of general deterioration of the lightsignal on the measured data.

In the rapidly evolving field of nano-biotechnology, manipulation ofparticles and objects are important issues. Tools for manipulation areatomic force microscopes, magnetic tweezers, photonic force microscopes(optical tweezers), micro needles, electric fields and field cages, andlevitated liquid droplets. To control these manipulation tools,secondary light emitted by the particle is often used as a feedbacksignal.

Apart from the manipulation of sample components, the characterizationof samples plays an important role in chemistry, physics, biology, andmedicine. Typical applications are chemical analysis in medicine,forensic science, material science, diagnostics, and biotechnology.Furthermore, in pre-clinical drug development, biological targetmolecules are examined in screening processes to identify compoundsinteracting with said target molecules. Very often ligand-receptor,substrate-enzyme, protein-protein, protein-DNA or protein-cell-membraneinteractions are studied. Such studies are often conducted utilizingsecondary light emission as a read-out. The information of emittedsecondary light can e.g. be used to produce images of the sample understudy. Presently, primarily fluorescence intensity is used in imaging.Further secondary light emission parameters such as fluorescencelifetime, anisotropy or polarization, or ratios of intensities fromdifferent wavelengths are also often used.

In the following, the word “light” will sometimes be used instead of“radiation”. The word “light” shall not be constrained as being limitedto visible radiation unless otherwise specified.

The excitation of a sample under study can e.g. take place by radiationas single-photon excitation, two-photon excitation or multi-photonexcitation, or by chemical reactions. The light used for inducing asecondary light emission may be continuous or sinusoidally modulated,e.g. for phase modulation measurements, or it may be a series of lightpulses. The scattering or emission of secondary light after excitationby primary light can be an elastic process, like Rayleigh-, Mie-, andRaman-scattering (e.g. Surface-Enhanced-Raman-Scattering (SERS) orSurface-Enhanced-Resonance-Raman-Scattering (SERRS)), or an inelasticprocess, e.g. luminescence such as phosphorescence or fluorescence.These processes are typically induced by directing electromagneticradiation (e.g. appropriate laser light) as primary light onto thesample. Whereas elastic emission is a temporally prompt process,inelastic emission is generally delayed with respect to the excitationtime. In case of luminescence, the probability of electronicdeactivation and hence the inelastic emission of light is temporallyexponentially distributed. The lifetime of the electronically excitedstate is defined as the time where the probability to be in the excitedstate has dropped to 1/e.

The detection of secondary emitted light can e.g. be performed on anepi-illuminated confocal fluorescence microscope using avalanchephotodiodes as described in detail previously [Kask, P., Palo, K., Fay,N., Brand, L., Mets, Ü., Ullman, D., Jungmann, J., Pschorr, J. and Gall,K. (2000) Two-Dimensional Fluorescence Intensity Distribution Analysis:Theory and Applications. Biophys. J., 78, 1703-1713]. Thereby, theexcitation light can be in a stationary position, or be moved over andscanning the sample. Further possible set-ups are evanescent-excitation,Raman microscopes, near-field microscopes, scanning (e.g. near-field orconfocal) microscopes using beam-scanners, table-scanners, and/orNipkov-devices, as well as spectrometers using non-confocal excitationand detection. Detection might also be performed on the opposite side ofthe excitation. Also, the detector does not necessarily have to be anavalanche photodiode. Any sensitive detector such as photo-multipliersor CCD-cameras will do.

To be detected due to emitted secondary light, the particle of interesteither has to have the ability to emit light by itself or has to belabeled by a secondary light emitting tag, e.g. a fluorescent dye, aluminescent nanoparticle (e.g. a semiconductor quantum dot), or a metalchelate. In general, the particles of interest are observed in a mediumsuch as in a solution, on surfaces, on cells, or in matrices. In drugscreening processes, typically the interaction between a biologicaltarget and a luminescent ligand in the presence of low molecular weightcompounds is studied. The biological target is typically involved in thepathogenesis of a disease and the compounds are screened to findpossible drug candidates. In one typical experimental set-up, theinfluence of the compounds on the binding reaction between ligand andtarget is studied utilizing secondary light emission as a read-out.

There are two main disadvantages when using secondary emitted light suchas fluorescence to perform characterizations of biological and/orchemical samples.

-   (1) “Auto-fluorescence”: Background light might occur due to    additional secondary light emitting particles in the sample besides    the particles of interest. These particles might be the medium    itself, i.e. solvent or surface molecules, impurities, and/or the    added compounds. In the field of fluorescence detection, this    phenomenon is known as auto-fluorescence. The auto-fluorescence    interferes the detected signal which does not solely consist of    actual light from the particles of interest anymore. Since the    interfering auto-fluorescence has its own characteristics, the    read-out (such as—intensity, anisotropy, brightness, or lifetime)    will be deteriorated. Let us consider the following example for    illustration purposes only:    -   The particles of interest might emit light with intensity I₁=120        kHz, anisotropy r₁=0.15, brightness q₁=30 kHz, and lifetime τ₁=3        ns.    -   The unwanted auto-fluorescence might have an intensity I₂=80        kHz, anisotropy r₂=0.05, brightness q₂=2 kHz, and lifetime τ₂=1        ns.    -   Then the non-deteriorated read-out without interfering        auto-fluorescence would be I_(tot)=120 kHz, anisotropy        r_(tot)=0.15, brightness q_(tot)=30 kHz, and lifetime r_(tot)=3        ns.    -   The deteriorated read-out with interfering auto-fluorescence        could be approximated via the fraction of background light,        f₂=I₂/I_(tot)=0.4 (with I_(tot)=I₁+I₂), with        x_(tot)=x₁×(1−f₂)+x₂×f₂ (with x=r, q, τ); thus, I_(tot)=200 kHz,        anisotropy r_(tot)=0.11, brightness q_(tot)=18.8 kHz, and        lifetime τ_(tot)=2.2 ns.

Samples with auto-fluorescence will therefore exhibit an increased lightintensity. However, the scientist might not know that the secondarylight contains spurious elements due to auto-fluorescence. The correctcharacterization of the particles of interest via the characteristics ofthe emitted light will be deteriorated and will fail.

-   (2) “Quenching”: In the case of tagged particles, the added    compounds might directly react with the secondary light emitting tag    and not with the tagged particle itself. This reaction might lead to    a change in the secondary light emission, mainly a decrease in light    intensity. Possible reactions can be ground-state and excited-state    complexes. In the field of fluorescence, this phenomenon is known as    quenching. Thus, changes and characteristics in the secondary    emitted light do not come from variations or properties of the    tagged particle of interest anymore, but from the quenching reaction    between compound and secondary light emitting tag. Again, let us    consider an example for illustration purposes only:    -   Fluorescently labeled peptides might emit light with intensity        I₁=100 kHz, anisotropy r₁=0.08, brightness q₁=30 kHz, and        lifetime τ₁=3 ns. Upon binding to a protein, the characteristics        of the emitted light might change to I₂=50 kHz, anisotropy        r₂=0.20, brightness q₂=15 kHz, and lifetime τ₂=1.5 ns    -   The binding might be activated by certain compounds. Thus, an        activating compound could directly be observed by the        characteristics of the emitted light due to the changes caused        by the binding event.    -   However, imagine a non-activating compound which directly        quenches the fluorescent tag. This compound might also induce        changes in the emitted light, e.g. a decreased intensity and        brightness, although no binding event occurred. From the        characteristics of the read-out an alleged activation would be        observed.

Samples with quenching compounds will exhibit a change in the emittedlight, mainly a decreased light intensity. Again, the correctcharacterization of the tagged particles of interest via thecharacteristics of the emitted light will be deteriorated and will fail.

In addition to the above described cases of auto-fluorescence andquenching, a general deterioration of the signal might occur e.g. due tosample handling mistakes such as pipetting errors or due to bleachingeffects of fluorescent dyes. A dye is bleached if the exciting light iscausing an irreversible or reversible reaction. This reaction leads to achange in the light emission e.g. by a destruction of the dye. In thecase of a destruction, the dye would irreversibly loose its ability toemit light.

In particular, in the field of high throughput drug screening, adeteriorated signal will have a severe impact on the furtherpre-clinical and clinical development. False positive compounds might befurther optimized with high technical and financial efforts. Falsenegative compounds might never become drugs because they have not beenidentified in the primary screening process. Of course, also indiagnostic and forensic applications interfering secondary lightemission might have severe impacts on the data and therefore on theoutcome of an experiment.

It is therefore an object of the present invention to improve thereliability of experimental data, in particular to improve the lightemission read-out with respect to impacts of interfering effects onsecondary light emission, in particular deterioration such asauto-fluorescence or quenching. This object is solved by the inventionaccording to the independent claims. Advantageous embodiments of theinvention are characterized in the dependent claims.

According to the present invention, a method is provided for identifyingthe impacts of interfering effects on experimental data. The methodcomprises the steps of:

-   -   (i) providing experimental data,    -   (ii) determining values of one or a plurality of identification        parameters from said data,    -   (iii) creating a histogram or distribution of the values of the        identification parameters,    -   (iv) determining one or a plurality of thresholds for the values        of identification parameters from said histogram or        distribution, which thresholds are indicative for the        interfering effects,    -   (v) analyzing the values of one or a plurality of identification        parameters whether or not these values fulfill one or a        plurality of criteria with respect to the thresholds, and    -   (vi) determining those data which are influenced and/or those        data which are not affected by the interfering effects.

In another aspect according to the present invention, a method isprovided for detecting the impacts of auto-fluorescence and/orfluorescence quenching on experimental data resulting from fluorescenceexperiments. The method comprises the steps of:

-   -   (i) providing the experimental data comprising a plurality of        data sets,    -   (ii) determining values of one or a plurality of identification        parameters from said data sets,    -   (iii) creating a histogram or distribution of the values of the        identification parameters,    -   (iv) determining one or a plurality of first thresholds for the        values of identification parameters from said histogram or        distribution, which first thresholds are indicative for        auto-fluorescence, and/or determining one or a plurality of        second thresholds for the values of identification parameters        from said histogram or distribution, which second thresholds are        indicative for fluorescence quenching,    -   (v) analyzing the values of one or a plurality of identification        parameters whether or not these fulfill one or a plurality of        criteria with respect to the thresholds, and    -   (vi) determining those data sets which are influenced and/or        those data sets which are not affected by auto-fluorescence        and/or fluorescence quenching.

In still another aspect of the present invention, a method is providedfor detecting false positive and/or false negative results inexperimental data. These data might result from screening of potentiallypharmaceutical active compounds. The data might also e.g. result fromdiagnostic tests or forensic studies. The method comprises the steps of:

-   -   (i) providing the data,    -   (ii) determining values of one or a plurality of identification        parameters from said data,    -   (iii) creating a histogram or distribution of the values of the        identification parameters,    -   (iv) determining one or a plurality of first thresholds for the        values of identification parameters from said histogram or        distribution, which first thresholds are indicative for a        false-positive result, and/or determining one or a plurality of        second thresholds for the values of identification parameters        from said histogram or distribution, which second thresholds are        indicative for a false-negative result,    -   (v) analyzing the values of one or a plurality of identification        parameters whether or not these fulfill one or a plurality of        criteria with respect to the thresholds, and    -   (vi) determining those data which represent a false-positive        result and/or those data which represent a false-negative        result.

In still another aspect, the invention provides a system for detectingthe impacts of interfering effects on experimental data resulting fromoptical experiments. The system comprises:

-   -   (i) means for supporting one or a plurality of samples in an        inspection station,    -   (ii) one or a plurality of photosensitive detectors which are        positioned relative to the inspection station so that        electromagnetic radiation emitted from the samples impinges on        the detectors,    -   (iii) means for addressing the photosensitive detectors to        generate experimental data,    -   (iv) means for determining values of one or a plurality of        identification parameters from said data,    -   (v) means for storing the values in such a manner that        preferably all the values which relate to any one of the samples        are linked,    -   (vi) means for creating a histogram or distribution of the        values of the identification parameters,    -   (vii) means for determining one or a plurality of thresholds for        the values of identification parameters from said histogram or        distribution, which thresholds are indicative for the        interfering effects,    -   (viii) means for analyzing the values of one or a plurality of        identification parameters whether or not these fulfill one or a        plurality of criteria with respect to the thresholds, and    -   (ix) means for supplying as output information those data which        are influenced and/or those data which are not affected by the        interfering effects.

In a preferred embodiment, the identification parameter is selected fromthe group consisting of a fluorescence intensity, a ratio offluorescence intensities at selected wavelengths, a ratio offluorescence intensities at different polarization directions, afluorescence anisotropy, a fluorescence polarization, a fluorescencelifetime, a rotational correlation time, a diffusion constant, aconcentration of fluorophores, and a specific fluorescence brightness.In another preferred embodiment, a function of the aforementionedmembers of the group might be chosen as an identification parameter.

The most simple identification parameters are:

-   (a) The signal count rate, denoted intensity, I. Consequently, the    experimental data can be checked whether they fulfill certain    criteria as follows. The values of the identification parameter, in    the present case the intensity values, can be checked with respect    to one or a plurality of thresholds for the values of the intensity    as an identification parameter, e.g. a pre-selected intensity value    and/or intensity function.-   (b) The anisotropy, r, or polarization, P. When employing two    detectors which monitor different polarization directions of the    emitted light, r and P can be calculated from the intensities with    parallel, I_(P), and perpendicular, I_(S), polarization with respect    to the polarization of the exciting light. Consequently, the data    resulting from these optical experiments can be checked with the    help of the identification parameter whether they fulfill certain    criteria with respect to one or a plurality of thresholds. In the    present case, these thresholds can be pre-selected anisotropy or    polarization values. Anisotropy and polarization are typically    defined as follows:

r=(I _(P) −I _(S))/(I _(P)+2I _(S)) P=(I _(P) −I _(S))/(I _(P) +I _(S))

-   (c) The ratio of intensities, f. When employing at least two    detectors which monitor different wavelength ranges or different    polarization directions of the emitted light, ratios or fractions of    intensities detected on one or more detectors can be deduced. These    can be checked whether they are in consistence with a threshold,    such as a pre-selected value and/or function of intensity ratios.-   (d) The lifetime, τ. The mean excitation-to-detection delay time,    i.e. the lifetime of the excited state of the secondary light    emitting particle can e.g. be measured using pulsed light excitation    together with time-correlated-single-photon-counting (TCSPC) or    using modulated light excitation in general. The lifetime can be    determined by a fit to the excitation-to-detection delay time    histogram and even enables to distinguish secondary light emitting    particles with different lifetimes within a mixture and to quantify    them via their fractional intensities; this can preferably be done    by performing a multi-component fit to the excitation-to-detection    delay time histogram. Again, a check of the values of the    identification parameters of the experimental data with regard to a    pre-selected lifetime value and/or function can be conducted.-   (e) The rotational correlation time, r. The rotational correlation    time is directly linked to the rotational diffusion of the light    emitting particles and is therefore a very nice tool to distinguish    molecules of different rotational diffusion e.g. due to different    mass. It can for example be determined using time-resolved    anisotropy analysis. Time-resolved anisotropy is based on the same    measurement principle as in the lifetime analysis. One can determine    the rotational correlation time by globally analyzing the two    excitation-to-detection delay time histograms recorded in the two    different detection channels monitoring different polarization    directions of the emitted light. This analysis enables to    distinguish secondary light emitting particles with different    lifetimes and/or rotational correlation times within a mixture and    to quantify them via their fractional intensities; this can    preferably be done by performing a multi-component fit to the    excitation-to-detection delay time histograms. Again, a check of the    values of the identification parameters of the experimental data    with regard to a pre-selected lifetime and/or rotational correlation    time value and/or function can be conducted.

More complex spectroscopic techniques have been developed which arebased on the detection of single light emitting particles and whichenable to resolve different light emitting particles within the samesample.

-   (a) The direct observation of signal bursts from single light    emitting particles enables to qualitatively and quantitatively    identify different light emitting particles in a mixture via their    spectroscopic properties; e.g. such as realized in a dye mixture    using the differing fluorescence properties: lifetime [Zander, C.,    Sauer, M., Drexhage, K. H., Ko, D. S., Schulz, A., Wolfrum, J.,    Brand, L., Eggeling, C. and Seidel, C. A. M. (1996) Detection and    characterization of single molecules in aqueous solution. Appl.    Phys. B, 63, 517-523], lifetime and intensity [Fries, J. R., Brand,    L., Eggeling, C., Köllner, M. and Seidel, C. A. M. (1998)    Quantitative identification of different single-molecules by    selective time-resolved confocal fluorescence spectroscopy. J. Phys.    Chem. A, 102, 6601-6613], and anisotropy [Schaffer, J., Volkmer, A.,    Eggeling, C., Subramaniam, V., Striker, G. and    Seidel, C. A. M. (1999) Identification of single molecules in    aqueous solution by time-resolved fluorescence anisotropy. J. Phys.    Chem. A, 103, 331-336]. Accordingly, a suitable identification    parameter as well as its value being indicative for a certain effect    is chosen. The experimental data can be checked with the help of one    or a plurality of corresponding identification parameters (e.g.    lifetime; lifetime and intensity; anisotropy) for fulfilling certain    criteria with respect to one or a plurality of thresholds, such as a    pre-selected value of lifetime.-   (b) FCS (fluorescence correlation spectroscopy) analyses the    temporal characteristics of signal fluctuations from single light    emitting particles. The calculated correlation function of these    fluctuations decays with time constants that are characteristic of    the molecular processes-causing these signal changes, e.g. diffusion    into and out of the detection volume and reaction kinetics. The    amplitude of the decay is related to the molecular concentration    while the inflection point of the correlation function represents    the mean diffusion time, τ_(diff), of the fluorescing molecules    through the detection volume, which is dependent on the diffusion    coefficient. Hence, by a fit to the correlation function, FCS is    able to resolve components of a sample with different diffusion    coefficients due to their different molecular masses; in practice,    preferably a multi-component fit to the correlation function is    performed. Accordingly, when studying FCS data, a suitable    identification parameter is the diffusion coefficient.-   (c) FEDA or 1D-FIDA (fluorescence intensity distribution analysis)    relies on a collection of instantaneous values of the fluctuating    intensity by building up a frequency histogram of the signal    amplitudes throughout a measurement [Kask, P., Palo, K., Ullman, D.    and Gall, K. (1999) Fluorescence-intensity distribution analysis and    its application in biomolecular detection technology. Proc. Natl.    Acad. Sci. U.S.A., 96, 13756-13761]. The resulting distribution of    signal intensities is then analyzed by a theory which relates    specific fluorescence brightness q (intensity per molecule in kHz),    and absolute concentration c (mean number of molecules in the    detection volume) of the molecules under investigation. By    performing a fit to the frequency histogram, FIDA distinguishes    species of the sample according to their different values of    specific molecular brightness q; in practice, preferably a    multi-component fit is performed. Consequently, when studying data    collected by FIDA experiments, a suitable identification parameter    is the specific molecular brightness q.-   (d) Further methods such as 2D-FIDA (two-dimensional fluorescence    intensity distribution analysis), FIMDA (fluorescence intensity    multiple distribution analysis), or FILDA (fluorescence intensity    and lifetime distribution analysis) might be applied resulting in    improved performance compared to the FIDA technique.    -   2D-FIDA typically makes use of a two-detector set-up monitoring        either different polarization or emission bands of the signal.        In addition to the FIDA performance, 2D-FIDA achieves additional        molecular resolution by performing a multi-component fit to the        two-dimensional frequency histogram of the concurrent signal        amplitudes from both detectors and, thus, the concurrent        determination of two specific brightness values of each        detection channel, q₁(channel1) and q₂(channel2), for each        component [Kask, P., Palo, K., Fay, N., Brand, L., Mets, Ü.,        Ullman, D., Jungmann, J., Pschorr, J. and Gall, K. (2000)        Two-Dimensional Fluorescence Intensity Distribution Analysis:        Theory and Applications. Biophys. J, 78, 1703-1713]. By        observing the molecular resolved anisotropy, simple and more        complex binding events and enzymatic reactions may be followed.        Alternatively, such events may be followed using light emitting        particles with different emission bands and combining this with        either two-color excitation by different lasers or        energy-transfer interaction. Thus, the use of a second detector        can improve the power of FIDA to distinguish between molecular        components and is, therefore, increasingly applied in        high-performance drug discovery. When analyzing data collected        by 2D-FIDA experiments, one will preferably choose two        identification parameters: (i) a molecular brightness, q₁, at a        first wavelength and/or a molecular brightness, q₂, at a second        wavelength; alternatively (ii) a molecular brightness, q₁, at a        first polarization and/or a molecular brightness, q₂, at a        second polarization.    -   FIMDA typically only demands one detection channel and extracts        all characteristics of both FCS and FIDA, i.e. diffusion time,        τ_(diff), specific molecular brightness, q, and absolute        concentration, c, from a single measurement [Palo, K., Mets, Ü.,        Jäger, S., Kask, P. and Gall, K. (2000) Fluorescence Intensity        Multiple Distribution Analysis: Concurrent Determination of        Diffusion Times and Molecular Brightness. Biophys. J 79]. This        is achieved by fitting a series of different FBDA histograms        obtained from the same measurement regarding different        components. FIMDA increases the readout and improves likelihood        of molecular resolution of different components of the sample        effectively by one dimension. Therefore, when analyzing data        collected by a FIMDA experiment, one will typically choose as        identification parameters a specific molecular brightness and/or        a diffusion time.    -   FILDA as well typically only demands one detection channel and        extracts all characteristics of both FIDA and lifetime        determination, i.e. specific molecular brightness, q, lifetime,        τ, and absolute concentration, c, from a single measurement.        FILDA is based on fitting a two-dimensional histogram of the        number of photons detected in counting time intervals of given        width and the sum of excitation-to-detection delay times of        these photons, once again regarding and quantifying different        fluorescent components. The combined information yielded by        FILDA results in significantly increased accuracy compared to        that of FIDA and lifetime analysis alone. Consequently, when        analyzing FILDA data, one may choose as identification        parameters a lifetime and/or a specific molecular brightness.

In all of the above methods, which can quantify each component by itsconcentration, c, or an according amplitude, the concentration oramplitude can as well be taken as an identification parameter.

With regard to optical experiments, the most simple identificationparameter for auto-fluorescence, fluorescence quenching, and/or othergeneral deterioration of the measured data (e.g. through measuringerrors, dispensing or pipetting errors, etc) is the light intensity,since these sorts of deterioration lead to a change of theexperimentally determined intensity; in principle, an increase isassumed in the case of auto-fluorescence and a decrease in the case offluorescence quenching.

However, since auto-fluorescence and quenching change the wholecharacteristic of the emitted and, thus, detected light, other read-outparameters can as well be used for identification. These are for exampleanisotropy (r), polarization (P), ratio of intensities (f), lifetime(τ), rotational correlation time (ρ), brightness (q), concentration (c),brightness values of different detection channels (q₁ and q₂), diffusiontime (τ_(diff)), or other parameters resulting from a fit to thelifetime histogram, correlation function (FCS), FIDA-, or otherhistogram techniques (e.g. 2D-FIDA, FIMDA, or FILDA).

The identification parameter can preferably be a quality parameter ofsuch a fit such as a chi²-value, which is for example calculated from

${chi}^{2} = {\sum\limits_{x}{{W(x)}\left\lbrack {{\overset{\Cap}{P}(x)} - {P(x)}} \right\rbrack}^{2}}$

(where the sum is performed over all data points x, {circumflex over(P)}(x) is the measured data, P(x) the theoretical data, and W(x) arethe weights, e.g. expressed as W(x)=M/P(x) with the total number of datapoints, M).

Further possible identification parameters result from a moment-analysisto the lifetime histogram, correlation function (FCS), FIDA-, 2D-FIDA-,FIMDA-, or FILDA-histogram by calculating moments, correlations,cumulants, and functions of these such as

-   -   M₁ ²/(M₂−M₁ ²), (M₂−M₁)/(M₁T), and K₃×0.55×K₁/K₂ ² with the        first and second moments, M₁ and M₂, and the first, second, and        third factorial cumulants, K₁, K₂, and K₃, resulting from a        one-dimensional function such as the FIDA-histogram.    -   |K₁₀+K₀₁−(K₂₀+2K₁₁+K₀₂)²/[(K₃₀+3K₂₁+3K₁₂+K₀₃)×0.55]| with the        factorial cumulants, K_(xy), resulting from a two-dimensional        function such as the 2D-FIDA-histogram.

In the case of observing images of the sample, additional identificationparameters besides the ones mentioned above might come from all kinds ofpattern recognition or image analysis algorithms as well as from imagemoment analysis.

The values of several identification parameters can also be linked toobtain a new value of a single identification parameter. Examples aremathematical procedures such as the calculation of vector lengths or ofgeneralized square distances.

Further identification parameters can also be obtained by relating oneor more of the above parameters to their values obtained from controlsamples such as

[X(sample)−X(control B)]/[X(control A)−X(control B)] or

[X(control A)−X(sample)]/[X(control A)−X(control B)]

where X(sample) is the identification parameter obtained from the sampleand X(control A) and X(control B) are the identification parametersobtained from the two different control samples. For example, in thecase of the binding of a tagged ligand to a protein, the latter relationexpresses the inhibition of the binding if the control A samplerepresents the complete binding event and the control B the free ligand.

Data from an experiment can be classified to have been influenced byauto-fluorescence, quenching, and/or general deterioration, if e.g. thevalue of at least one of the above identification parameters determinedfrom the emitted and detected light of this sample is above or below apre-selected threshold value. In case a set of various identificationparameters is used for classification, the classification rules might bethat the values of all of the identification parameters have to be aboveor below certain corresponding threshold values, that only the value ofat least one identification parameter has to be above or below a certaincorresponding threshold value, or that the set of identificationparameters have to fulfill certain functions or relations between thecorresponding parameters. For example, if two identification parameters,x₁ and x₂, are used, the classification rules might be

-   (a) [x₁>t_up(x₁) and/or x₁<t_low(x₁)] and/or [x₂>t_up(x₂) and/or    x₂<t_low(x₂)] with upper and lower threshold values, t_up and t_low,    of x₁ and x₂, respectively.-   (b) x₂>f₁(x₁) and/or x₂<f₂(x₁) where f₁ and f₂ are functions of one    of the parameters such as f_(i)(x₁)=a_(i)+m_(i)×x₁ with i=1 and 2    and constants a_(i) and m_(i).

The pre-selected threshold values, functions and/or relations can bedetermined from the values of identification parameters obtained from awhole set of observed samples. The threshold values, functions and/orrelations can be determined from the one-dimensional distribution of thevalues of one identification parameter or from the multi-dimensionaldistribution of the values of a set of concurrent identificationparameters as obtained from all or parts of the set of observed samples.

The values of the distribution of identification parameters asdetermined from the set of observed samples can also be mathematicallytransformed or normalized to yield special properties of thedistribution such as Gaussian distributions, e.g. by calculatingstandardized or studentized residuals.

The set of observed samples (and consequently the gathered experimentaldata or data sets) can either be all or parts of the samples to beanalyzed and/or all or parts of control samples. Histograms ordistributions of the values of the identification parameters cantherefore be created from all of the data sets, e.g. including alsocontrol samples, or only parts thereof.

The threshold values, functions and/or relations can be derived from thedistribution through functions of the mean, median, moments, cumulants,standard deviation, and/or the values themselves of the distribution. Apossible function would be mean ±y×s or median ±y×s*, where y is aconstant, e.g. 3, and mean and median are the mean and median of thedistribution, respectively, s is the standard deviation of thedistribution, and s* represents the median like standard deviation ofthe distribution. s* can either be obtained by (a) cutting of x % of theedges of the distribution (i.e., disregarding the x % highest and lowestvalues, x is a constant and can e.g. be 1) and calculating the commonstandard deviation of the remaining distribution, (b) calculating themedian of the absolute differences between each point and the median ofthe distribution and multiplying this value by 1.482, (c) fitting atheoretical distribution which is a function of s* to the experimentaldistribution, such as the Gaussian distribution(G(x)=A×exp(−2(x−x₀)²/(2s*²)) with an amplitude, A, and the mean, x₀ or(d) taking the mean of the values calculated for s, in (a), (b), and/or(c). The constant, y, can be set by hand after observation of thedistribution or derived from theory. For example, if the distribution ofidentification parameters is Gaussian-like or has been mathematicallytransformed or normalized to a Gaussian-like distribution, the thresholdis best set to y=3. In this case, the probability to be a valid part ofthe distribution is 99.8%, if the value of an identification parameteris within the threshold (95.5% for y=2 and 70.5% for y=1).

For example, if only one identification parameter is chosen foridentification, the corresponding one-dimensional distribution can bebuilt up from the values of the identification parameter obtained fromseveral samples and be transformed to a Gaussian distribution, and theupper and lower threshold values be determined as mean +3×standarddeviation and mean −3×standard deviation, respectively.

For example, if two identification parameters are chosen, one can

-   a) build up the corresponding two-dimensional distribution from the    values of the identification parameters obtained from a set of two    different control samples (control A and control B),-   b) determine the mean values (m(control A,x_(i)), m(control    A,x_(i))) from each set of the two control samples for each    identification parameter i=1 and 2,-   c) determine upper (t_up(control A,x_(i)), t_up(control B,x_(i)))    and lower threshold values (t_low(control A,x_(i)), t_low(control    B,x_(i))) from each set of the two control samples for each    identification parameter i=1 and 2 (e.g. by calculating mean    +3×standard deviation and mean−3×standard deviation, respectively,    for each set), and-   d) classify auto-fluorescence, quenching, and/or general    deterioration according to the condition;

x ₂ <a ₁ +m ₁ ×x ₁ or

x ₂ >a ₂ +m ₂ ×x ₁ or

[x ₁ <t_low(control A,x ₁) and x ₁ <t_low(control B,x ₁)] or

[x ₁ >t_up(control A,x ₁) and x ₁ >t_up(control B,x ₁)]

-   -   with        -   m₁=[t_low(control A,x₂)−t_low(control B,x₂)]/[m(control            A,x₂)−m(control A,x₂)],        -   a₁=t_low(control A,x₂)−m₁×m(control A,x₂)        -   m₂=[t_up(control A,x₂)−t_up(control B,x₂)]/[m(control            A,x₂)−m(control A,x₂)],        -   a₂=t_up(control A,x₂)−m₂×m(control A,x₂).

Preferably, one of the identification parameters should be the intensity(I) (or the intensity normalized to the intensity obtained from controlsamples as described above), whereby auto-fluorescence is identified byan increased intensity, whereas fluorescence quenching and/or generaldeterioration is identified by a decreased intensity.

The identification step can of course not only be applied to the saididentification of general deterioration but in general to check thefailure of any signal or analysis method such as a fit. For example, theresults of any lifetime, FCS, FIDA, or further histogram-based analysiscan be checked in this manner for a failure.

In summary, the identification step relating to data gathered fromfluorescence measurements is preferably performed in three steps.

-   1. Selection of at least one appropriate identification parameter,    one of which is preferably intensity (I) or normalized intensity.-   2. Determination of pre-selected threshold values, functions, and/or    relations from the values of said chosen parameters.-   3. Identification of auto-fluorescence and/or fluorescence quenching    according to conditions specified by the said threshold values,    functions, and/or relations.

It is particularly preferred to conduct after the identification of databeing influenced by auto-fluorescence, fluorescence quenching, and/orgeneral deterioration, the following steps:

-   1. Correction of the read-out—in the case of auto-fluorescence with    the goal to separate auto-fluorescence from the light emitted by the    particles of interest.-   2. Test-procedure to check whether the correction procedure has    succeeded.

The correction step is performed for correcting the signal to typicallyseparate interfering signal and extract only the information coherentwith the light emitted from the particles of interest.

The correction step for interfering auto-fluorescence signal preferablydemands a read-out which is able to distinguish secondary light emittingparticles with different emission characteristics within the same sampleand to quantify them using a multi-component analysis, i.e. whichmolecularly resolves the detected light. As mentioned above, read-outmethods that are capable of this molecular resolution are e.g. thelifetime determination, FCS, FDA, and further histogram-based methodssuch as time-resolved anisotropy, 2D-FIDA, FIMDA, or FILDA. Theseread-out methods enable to apply a multi-component fit to the functionsor histograms obtained from the detected light and, thus, to resolvedistinguishable light emitting particles.

Furthermore, the lifetime analysis based methods (lifetime determinationand FILDA) enable to distinguish between an elastic light emissionprocess such as scattering and an inelastic emission process such asluminescence, since the elastic emission is a temporally prompt process(lifetime τ=0 ns) while the inelastic emission is generally delayed withrespect to the excitation time (τ>0 ns). Therefore, lifetime analysisoffers the possibility to explicitly regard elastic light emittingparticles.

In contrast, all brightness-based methods allow to explicitly regardlight emitting particles with high concentration and very low brightness(detected counts per particle) such as scattering solvent particles,since the signal amplitudes originating from such components are simplyPoissonian distributed. Such a signal shall be denoted FDA-backgroundlater on. In general, this FIDA-background is fixed to a pre-selectedvalue, e.g. determined from control experiments.

The correction procedure is applied by preferably adding an additionalcomponent to the fit, which accounts for the additionalauto-fluorescence “component”. Thus, the remaining components/light arecleared from the interfering auto-fluorescence light. According to theread-out method, this additional component might be accounted for in thefit as follows:

-   -   lifetime determination: an additional lifetime, which is either        fixed to a pre-selected value or freely fitted, and/or a freely        fitted amount of elastic light emitting particles.    -   FCS: an additional diffusion time, which is either fixed to a        pre-selected value or freely fitted;    -   FIDA: an additional brightness, which is either fixed to a        pre-selected value or freely fitted, and/or a freely fitted        FIDA-background.    -   2D-FIDA: an additional pair of brightness values, where either        both values are fixed to pre-selected values, only one value is        fixed while the other is subject to fitting, or both values are        subject to fitting, and/or a pair of FDA-background values,        where either both values are fixed to pre-selected values, only        one value is fixed while the other is subject to fitting, or        both values are subject to fitting.    -   histogram-based methods in general: an additional set of        read-out parameters (e.g. brightness, lifetime, diffusion time,        and/or FIDA-background), where either all values are fixed to        pre-selected values, only at least one value is fixed while the        others are subject to fitting, or all values are subject to        fitting.

In general, the light emission of the auto-fluorescent particles israther weak compared to the actual light emitting particles of interest.Therefore, their brightness can be assumed to be rather low, and, iffixed within a FIDA(-based) fit, the brightness-values of the additionalauto-fluorescent component can be fixed to a rather low value (e.g. >0kHz to <10 kHz).

Furthermore, auto-fluorescent particles are very often highlyconcentrated within the sample (e.g., in screening and HTS applicationscompounds are added in μM-concentration, while the light emittingparticles of interest are concentrated at least below 50 nM forFIDA-based applications). Therefore, the auto-fluorescence emission isvery FEDA-background like and can be considered as FBDA-background inthe fit or as an additional component with its concentration value fixedto a rather high value (c>50).

Since an additional component might deteriorate the accuracy of theresults of the fit, it is very often preferable to apply thisauto-fluorescence correction procedure only to those data which areidentified to reveal auto-fluorescence properties. This means as ageneral rule that preferably a correction step is only performed onthose data which have been identified as being influenced by interferingeffects, such as auto-fluorescence or fluorescence quenching.

As outlined above, preferably a test procedure to check whether thecorrection step has succeeded is performed in the same manner as theidentification step described above. Basically, failed correctionprocedures are identified and marked as bad data points.

In a first step, at least one parameter is chosen which identifies thefailure of the correction procedure, denoted failure parameter. Thepotential failure parameters can be the same as previously described asidentification parameters in the identification step. Preferably, atleast one of the chosen failure parameters is a resulting value of theapplied fit, e.g., a quality parameter such as the chi² value, thelifetime, rotational correlation time, diffusion time, a brightnessvalue, or the concentration.

In a second step, threshold values, functions, and/or relations arespecified from the said chosen failure parameters as described in detailin the identification step, i.e. by determining them from thedistribution of values of failure parameters obtained from a whole setof observed samples or from subgroups thereof.

In a third step, a failure is classified according to conditionsspecified by the said threshold values, functions, and/or relations—aprocedure that is analogue to the identification procedure. Thisprocedure has in detail been described above.

The test procedure can of course not only be applied to the saidcorrection procedure but in general to check the failure of any analysismethod such as a fit. For example, the results of any lifetime, FCS,FEDA, or histogram-based analysis can be checked in this manner for afailure.

Other objects, advantages, and novel features of the invention willbecome apparent from the following detailed description of the inventionwhen taken in conjunction with the accompanying drawings.

FIG. 1A-F illustrates impacts of the effect of quenching compounds onfluorescence emission data and presents different identificationmethods.

FIG. 2A-C illustrates impacts of the effect of auto-fluorescentcompounds on fluorescence emission data using polarization and 2D-FIDAread-outs and presents different identification methods, a correctionprocedure, and a procedure for checking possible failures of thecorrection step.

FIG. 3A-B illustrates impacts of the effect of auto-fluorescentcompounds on fluorescence emission data using FDA read-outs and presentsa correction procedure.

FIG. 4A-B illustrates screening for activating compounds usingpolarization and 2D-FIDA read-outs and the identification ofauto-fluorescent and quenching compounds, a correction procedure, and acheck of the correction step in the case of auto-fluorescence.

FIG. 5A-B illustrates an identification of auto-fluorescent andquenching compounds in high-throughput-screening (HTS).

FIG. 6 shows a schematic diagram of a preferred system for detecting theimpacts of interfering effects on experimental data resulting fromfluorescence measurements.

EXAMPLES

The measurements presented in the following were performed on anepi-illuminated confocal fluorescence microscope as described in [P.Kask, K. Palo, N. Fay, L. Brand, Ü. Mets, D. Ullmaun, J. Jungmann, J.Pschorr, K. Gall (2000) Biophys. J, 78, 1703-1713]. A polarizedcontinuous-wave (cw) laser either at 543 nm or 633 nin was used toexcite a fluorophore (Tetramethyl-Rlhodamine (TAMRA) for 543 nmexcitation, MR-121 for 633 nm excitation) alone or covalently linked toa molecule of interest. Detection was performed with a single detectoror two detectors (Avalanche-Photo-Diode, APD) monitoring thefluorescence light emitted with parallel or perpendicular polarizationwith respect to the polarization of the exciting light. While theone-detector set-up was used for the fluorescence data analysis viaFIDA, the two-detector set-up was used for the determination of thepolarization or anisotropy values and analysis via 2D-FIDA.

Example 1

In a first measurement series, different amounts of various watersoluble chemical compounds were added to an aqueous TAMRA solution(about 15 nM, resulting in 96 different samples) and the totalintensity, I_(tot), as well as the polarization, P, were determined forthe 96 different samples (two-detector set-up and measurement time oftwo seconds). I_(tot) and P were calculated from the intensities withparallel, I_(P), and perpendicular, I_(S), polarization with respect tothe exciting light.

I _(tot) =I _(P)+2I _(S) P=(I _(P) −I _(S))/(I _(P) +I _(S))×1000

Subsequently, 4 different methods were applied to identify samples withdeteriorated signal as presented in FIG. 1.

FIG. 1A shows the one-dimensional distribution of I_(tot) over all 96samples (dotted line) together with the thresholds (vertical lines) foridentification of deteriorated signal. The thresholds were set accordingto the median, med(I_(tot)) 1119.9 kHz, and the median like standarddeviation, s*(I_(tot))=145.8 kHz, of I_(tot) from all 96 samples (s* hasbeen described previously);threshold(I_(tot))=med(I_(tot))±3×s*(I_(tot)). All samples thatexhibited a value of I_(tot) outside these thresholds were identified tobe deteriorated, in this case 12 samples.

FIG. 1B shows the one-dimensional distribution of the mathematicallytransformed total intensity, TI, together with a Gaussian fit to thedistribution (gray line; G(TI)=A×exp[−(TI−TI₀)²/(2σ²)] with thevariables A, TI₀, and σ subject to fitting and resulting in A=13.9,TI₀=0.44, and σ=0.3) and the thresholds (vertical lines) foridentification of deteriorated signal. The mathematical transformationwas performed according to the steps; (a) calculation of the median,med(I_(tot)), of I_(tot) from all 96 samples, (b) calculating thedifference, res(I_(tot))=I_(tot)−med(I_(tot)), for each sample, (c)determination of the mean, mean(res(I_(tot))), and the standarddeviation, s(res(I_(tot))), of res(I_(tot)) from all 96 samples, (d)calculating TI=[res(I_(tot))−mean(res(I_(tot)))]/s(res(I_(tot))). Thethresholds were determined from the resulting value of σ and TI₀ of theGaussian fit; threshold(TI)=TI₀±3×σ. All samples that exhibited a valueof TI outside these thresholds were identified to be deteriorated, inthis case 13 samples.

FIG. 1C shows the one-dimensional distribution of the slightly differentmathematically transformed total intensity, TI*, together with aGaussian fit to the distribution (gray line;G(TI*)=A×exp[−(TI*−TI₀*)²/(2σ*²)] with the variables A, TI₀*, and σ*subject to fitting and resulting in A=3.788, TI₀*=0.266, and σ=1.11) andthe thresholds (vertical lines) for identification of deterioratedsignal. In this case, the mathematical transformation was performedaccording to the steps; (a) calculation of the median, med(I_(tot)), ofI_(tot) from all 96 samples, (b) calculating the difference,res(I_(tot))=I_(tot)−med(I_(tot)), for each sample, (c) determination ofthe median, median(res(I_(tot))), and the median like standarddeviation, s*(res(I_(tot))), of res(I_(tot)) from all 96 samples, (d)calculating TI*=[res(I_(tot))−median(res(I_(tot)))]/s*(res(I_(tot))).The thresholds were determined from the resulting value of σ* and TI₀*of the Gaussian fit; threshold(TI*)=TI₀*+3×σ*. All samples thatexhibited a value of TI outside these thresholds were identified to bedeteriorated, in this case 12 samples.

FIG. 1D represents the two-dimensional distribution of joint totalintensity-polarization pairs, (I_(tot), P), from all 96 samples (blackdots) together with the thresholds (black lines) for identification ofdeteriorated signal. The thresholds were set according to the median,med(I_(tot))=1119.9 kHz and med(P)=30.72, and the median like standarddeviation, s*(I_(tot))=145.8 kHz and s*(P)=5.15, of I_(tot) and Prespectively from all 96 samples;threshold(I_(tot))=med(I_(tot))±3×s*(I_(tot)) andthreshold(P)=med(P)±3×s*(P). All samples that exhibited a value ofI_(tot) or P outside these thresholds were identified to bedeteriorated, in this case 13 samples.

By one or the other method, the same conspicuous samples were identifiedby a decreased intensity and an increased polarization. To find thereason behind this deterioration, one conspicuous compound was added atrising concentrations to the dye solution. The measured total intensity,I_(tot), and polarization, P, are shown in FIGS. 1E and F, respectively.One clearly observes, that the deterioration was caused by a quenchinginterference of the compound to the fluorescence emission of the dye,which was a decreasing intensity accompanied by an increase inpolarization.

Example 2

In a second measurement series, the binding of a small MR-121-labeledpeptide to the SH2-domain of the Grb2-protein was monitored by a changein the fluorescence polarization, P, of the MR-121 fluorescence emission(two-detector set-up, measurement time of ten seconds). In the differentsamples, the binding was increasingly inhibited by the titration ofunlabeled peptide. Thereby, nine different concentrations of unlabeledpeptide were measured five times each, i.e. 45 samples were observed.While one set of 45 samples only contained the assay components (labeledand unlabeled peptide and protein), auto-fluorescent compounds (1 μMRhodamine 800) had been added to another set of 45 samples. 2D-FIDA witha one-component fit was applied to the signal of all samples. Thisanalysis yielded values of concentration, c, brightness, q₁ and q₂, ofeach detection channel monitoring the light emission with parallel andperpendicular polarization with respect to the exciting light,respectively, and of chi², which is the quality parameter of the fit (aspresented previously). The total signal intensity was once againcalculated from the intensities with parallel, I_(P), and perpendicular,I_(S), polarization with respect to the exciting light, while thepolarization, P, was calculated from q₁ and q₂.

I_(tot)=I_(P)+2I_(S) P=(q₁−q₂)/(q₁+q₂)×1000

In addition, two control samples were measured ten times each, resultingas well in values of c, q₁ and q₂, chi², I_(tot), and P. The ten highcontrol samples, which contained only labeled peptide and protein(resulting in mainly bound labeled peptide), resulted in values ofc(high), q₁(high) and q₂(high), chi²(high), I_(tot)(high), and P(high).The low control, which contained labeled peptide, excess of unlabeledpeptide, and protein (resulting in totally inhibited binding, thusmainly unbound labeled peptide), resulted in values of c(low), q₁(low)and q₂(low), chi²(low), I_(tot)(low), and P(low). This enabled thecalculation of the normalized total intensity, NI, and the inhibition,Inh, for each measurement X.

NI(X)=[I _(tot)(X)−I _(tot)(low)]/[I _(tot)(high)−I _(tot)(low)]×100

Inh(X)=[P(high)−P(X)]/[P(high)−P(low)]×100

FIGS. 2A and B present two different methods how samples withauto-fluorescence can be identified.

FIG. 2A plots the two-dimensional distribution of joint normalized totalintensity—inhibition pairs, (NI, Inh), from both sets of 45 samples(black dots), the high samples (gray cross), and the low samples (graycircles) together with the threshold functions (black lines) for saididentification. The thresholds were set by the mean, m(NI,high)=0,m(Inh,high)=0, m(NI,low) 100, and m(Inh,low)=100, and the standarddeviation, s(NI,high)=19.7, s(Inh,high)=5.8, s(NI,low)=17.9, ands(Inh,low)=2.7, of NI and Inh from all ten high and low samples,respectively;

t _(—)1(Inh)=m(Inh,high)−3×s(Inh,high), t_(—)2(Inh)=m(Inh,low)+3×s(Inh,low),

t _(—)3(NI)=m(NI,low)−3×s(NI,low), t _(—)4(NI)=m(NI,low)+3×s(NI,low),

t _(—)5(NI)=m(NI,high)−3×s(NI,high), t_(—)6(NI)=m(NI,high)+3×s(NI,high),

An auto-fluorescent sample was identified if its read-out, NI or Inh,obeyed one of the following conditions;

Inh<t _(—)1(Inh), Inh>t _(—)2(Inh), NI<a ₁ +b ₁×Inh, or NI>a ₂ +b ₂×Inh,

-   -   with        -   a₁=t_(—)3(NI)−b₁×m(Inh,low), a₂=t_(—)4(NI)−b₂×m(Inh,low),        -   b₁=[t_(—)3(NI)−t_(—)5(NI)]/[m(Inh,low)−m(Inh,high)], and        -   b₂=[t_(—)4(NI)−t_(—)6(NI)]/[m(Inh,low)−m(Inh,high)].

In this way, all 45 samples with added auto-fluorescence wereidentified.

FIG. 2B plots the two-dimensional distribution of chi²-inhibition pairs,(chi², Inh), from both sets of 45 samples (black dots), the high samples(gray cross), and the low samples (gray circles) together with thethreshold functions (black lines) for said identification. Thethresholds were set by the mean, m(chi²,high)=1.37, m(Inh,high)=0,m(chi²,low)=0.71, and m(Inh,low)=100, and the standard deviation,s(chi²,high)=0.24, s(Inh,high)=5.8, s(chi²,low)=0.06, ands(Inh,low)=2.7, of chi² and Inh from all ten high and low samples,respectively;

t _(—)1(Inh)=m(Inh,high)−3×s(Inh,high), t_(—)2(Inh)=m(Inh,low)+3×s(Inh,low),

t _(—)3(chi²)=m(chi²,low)−3×s(chi²,low), t_(—)4(chi²)=m(chi²,low)+3×s(chi²,low),

t _(—)5(chi²)=m(chi²,high)−3×s(chi²,high), t_(—)6(chi²)=m(chi²,high)+3×s(chi²,high),

An auto-fluorescent sample was identified if its read-out, chi² or Inh,obeyed one of the following conditions;

Inh<t _(—)1(Inh), Inh>t _(—)2(Inh), chi² <a ₁ +b ₁×Inh, or chi² >a ₂ +b₂×Inh,

-   -   with        -   a₁=t_(—)3(chi²)−b₁×m(Inh,low),            a₂=t_(—)4(chi²)−b₂×m(Inh,low),        -   b₁=[t_(—)3(chi²)−t_(—)5(chi²)]/[m(Inh,low)−m(Inh,high)], and        -   b₂=[t_(—)4(chi²)−t_(—)6(chi²)]/[m(Inh,low)−m(Inh,high)].

Once again, all 45 samples with added auto-fluorescence were identified.

FIG. 2C shows the titration curves for the pure samples (black dots) andthe samples with added auto-fluorescence (transparent dots), i.e. thecurve shows the change of the polarization, P, with increasingly addedunlabeled peptide (the error bars were obtained from the results of thefive samples observed for each titration point). The effect of theauto-fluorescence on the detected fluorescence becomes evident by adecreased polarization value. Fitting the auto-fluorescent samples withan additional pair of floating FIDA-background values resulted in acorrection of the read-out. This is demonstrated in FIG. 2D, where thecorrected read-out of the auto-fluorescent samples coincides with theread-out of the pure samples.

FIG. 2E demonstrates the test procedure of the correction step. Similarto FIG. 2B, it plots the two-dimensional distribution of correctedchi²-inhibition pairs, (chi², Inh), from both sets of 45 samples (blackdots), the low samples (gray cross), and the high samples (gray circles)together with the threshold functions (black lines) for saididentification. The thresholds were identically set as in FIG. 2 B.

The failure of the correction step was identified if the accordingread-out, chi² or Inh, obeyed one of the following conditions;

Inh>t _(—)2(Inh), chi² <a ₁ +b ₁×Inh, or chi² >a ₂ +b ₂×Inh,

-   -   with        -   a₁=T_(—)3(chi²)−b₁×m(Inh,low),            a₂=t_(—)4(chi²)−b₂×m(Inh,low),        -   b₁=[t_(—)3(chi²)−t_(—)5(chi²)]/[m(Inh,low)−m(Inh,high)], and        -   b₂=[t_(—)4(chi²)−t_(—)6(chi²)]/[m(Inh,low)−m(Inh,high)].

In this way, only one failure of the correction step was identified.

Example 3

In a third measurement series, the binding of a TAMRA-labeled ligand tomembrane vesicles with the appropriate G-protein coupled receptors wasmonitored using FIDA (one-detector set-up, measurement time of twoseconds). The ligand bound to the vesicles can be distinguished from thefree ligand by an increase in the fluorescence brightness, q, since thevesicles can bind several ligands. In FBDA, these two components weredistinguished in a two-component fit and their brightness, q(ligand) andq(vesicle), and concentration values, c(ligand) and c(vesicle), weredetermined. For every sample the binding degree was determined accordingto the equation,

bindingdegree=c(vesicle)×q(vesicle)/[c(vesicle)×q(vesicle)+c(ligand)×q(ligand)].

48 high control and 48 low control sample were measured. The highcontrol contained both, labeled ligand and vesicles, while the lowcontrol solely contained labeled ligand. In a first set of measurements,the pure 96 samples were observed. In additional sets of measurement,the 96 samples were observed after adding different amounts ofauto-fluorescent substances (0.05 μM, 0.5 μM, and 1 μM of the dye C682);The binding degree resulting from the two-component FIDA fit is shown inFIG. 3A. The apparently decreased binding degree shows the effect of theincreasingly added auto-fluorescent substances.

For the correction, a three-component FIDA analysis was performed on thesame fluorescence data sets. Thereby, an additional component withfloating concentration, c(auto-fluorescence), and fixed brightnessvalue, q(auto-fluorescence)=1 kHz, was added to the two-component fit ofFIG. 3A. The fixed brightness value was rather low compared to the meanbrightness values obtained for the free ligand, q(ligand)=9 kHz, and theligand bound to the vesicle, q(vesicle)=1350 kHz. The resulting valuesof the binding degree coincides with that of the pure samples, whichindicates the success of the correction procedure. However, a decreasedaccuracy of the determination of the binding degree becomes evident bythe increased error bars, which is due to the presence of theinterfering auto-fluorescence as well as the correction procedure.Therefore, it is recommendable to apply this correction step solely tothose samples which are identified to emit auto-fluorescence.

Example 4

In a fourth measurement series, 96 different compounds were tested forthe activation of a DNA-binding protein. Upon activation, the proteinwas able to bind the single DNA strand. Since the DNA strand was labeledwith TAMRA, the activation was accompanied by an increase in thepolarization, P. A positive compound, which activated the protein,should therefore result in an increase of polarization, P. To check thereactivity of the compounds, the polarization read-out was compared tothat of positive and negative controls. While the negative control justlike a non-activating compound comprised the unbound DNA strand (lowpolarization), the positive control just like an activating compoundcomprised the DNA-peptide complex (high polarization). As in theprevious example 2, the measurements were performed with two detectorsmonitoring the different polarization directions of the light emissionand analyzed using 2D-FIDA regarding only one fluorescent component.This resulted in values of the intensity, I_(P) and I_(S), as well as ofthe brightness, q₁ and q₂, of the fluorescence with parallel andperpendicular polarization with respect to the polarization of theexciting light, respectively, and of the mean concentration, c, of thefluorescent component. This enabled the calculation of the totalintensity, I_(tot), the total brightness, q_(tot), the activation, Act,as well as the normalized total signal, NI.

I _(tot) =I _(P)+2I _(S) q _(tot) =q ₁ +q ₂ P=(q ₁ −q ₂)/(q ₁ +q ₂)×1000

NI(X)=[I _(tot)(X)−I _(tot)(pos)]/[I _(tot)(neg)−I _(tot)(pos)]×100

Act(X)=[P(X)−P(neg)]/[P(pos)−P(neg)]×100

The whole experiment included the measurement (two second duration) of96 different compounds added to the assay (labeled DNA and protein) aswell as nine positive controls and 96 negative controls.

For the identification of possible auto-fluorescent or quenchingcompounds, FIG. 4A plots the two-dimensional distribution of jointnormalized total intensity-activation pairs, (NI, Act), from the 96compound samples (black dots), the 96 negative control samples (graycross), and the six positive control samples (gray circles) togetherwith the threshold functions (black lines) for said identification. Inthe same way as in FIG. 2A, the thresholds were set by the mean,m(NI,pos)=0, m(Act,pos)=100, m(NI,neg)=100, and m(Act,neg) 0, and thestandard deviation, s(NI,pos)=4.1, s(Act,pos)=4.7, s(NI,neg)=9.3, ands(Act,neg)=6.2, of NI and Act from all six positive and 96 negativecontrol samples, respectively;

t _(—)1(Act)=m(Act,neg)−3×s(Act,neg), t_(—)2(Act)=m(Act,pos)+3×s(Act,pos),

t _(—)3(NI)=m(NI,neg)−3×s(NI,neg), t _(—)4(NI)=m(NI,neg)+3×s(NI,neg),

t _(—)5(NI)=m(NI,pos)−3×s(NI,pos), t _(—)6(NI)=m(NI,pos)+3×s(NI,pos).

An auto-fluorescent compound sample was identified if its read-out, NIor Act, was above the upper threshold line, i.e. obeyed the followingcondition;

NI>a ₂ +b ₂×Act,

-   -   with        -   a₂=t_(—)4(NI)−b₂×m(Act,neg), and        -   b₂=[t_(—)4(NI)−t_(—)6(NI)]/[m(Act,neg)−m(Act,pos)].

In this way, 67 compound samples were identified to be auto-fluorescent.

A quenching compound sample was identified if its read-out, NI or Act,was below the lower threshold line or elsewhere to the left or right ofthe two vertical lines, i.e. obeyed one of the following conditions andwas not auto-fluorescent;

Act<t _(—)1(Act), Act>t _(—)2(Act), or NI<a ₁ +b ₁×Act,

-   -   with        -   a₁=t_(—)3(NI)−b₁×m(Act,neg), and,        -   b₁=[t_(—)3(NI)−t_(—)5(NI)]/[m(Act,neg)−m(Act,pos)].

In this way, two compound samples were identified to be quenching andtaken away from further analysis (bad data points).

In a second step, the correction procedure was applied to thefluorescence data from the compound samples identified as beingauto-fluorescent (while the results of the analysis were maintained forthe valid compound samples). The correction procedure comprised a2D-FIDA fit regarding one component as before and in addition twofloating FIDA-background values as already applied in example 2. Thesuccess of the correction procedure was checked (see FIG. 4B). Similarto FIG. 2B, it plots the two-dimensional distribution of the correctedtotal brightness-activation pairs, (q_(tot), Act), from the 94 leftsamples (black dots), the 96 negative samples (gray cross), and the sixpositive samples (gray circles) together with the threshold functions(black lines) for the identification of failures of the correctionalgorithm or the analysis in principle. The thresholds were set by themean, m(q_(tot),pos)=68.0, m(Act,pos)=100, m(q_(tot),neg)=58.4, andm(Act,neg)=0, and the standard deviation, s(q_(tot),pos)=3.7,s(Act,pos)=4.7, s(q_(tot),neg)=3.5, and s(Act,neg)=6.2, of q_(tot) andAct from all six positive and 96 negative control samples, respectively;

t _(—)1(Act)=m(Act,neg)−3×s(Act,neg), t_(—)2(Act)=m(Act,pos)+3×s(Act,pos),

t _(—)3(q _(tot))=m(q _(tot) ,neg)−5×s(q _(tot) ,neg), t _(—)4(q_(tot))=m(q _(tot) ,neg)+5×s(q _(tot) ,neg),

t _(—)5(q _(tot))=m(q _(tot) ,pos)−5×s(q _(tot) ,pos), t _(—)6(q_(tot))=m(q _(tot) ,pos)+5×s(q _(tot) ,pos).

The said failure was identified if the according read-out, q_(tot) orAct, obeyed one of the following conditions;

Act>t _(—)2(Act), q _(tot) <a ₁ +b ₁×Act, or q _(tot) >a ₂ +b ₂×Act,

-   -   with        -   a₁=t_(—)3(q_(tot))−b₁×m(Act,low),            a₂=t_(—)4(q_(tot))−b₂×m(Act,low),        -   b₁=[t_(—)3(q_(tot))−t_(—)5(chi²)]/[m(Act,low)−m(Act,high)],            and        -   b₂=[t_(—)4(q_(tot))−t_(—)6(q_(tot))]/[m(Act,low)−m(Act,high)].

In this way, eight failures of the whole analysis process wereidentified.

Using the identification step and correction procedure, together withthe failure check, one can not only exclude possible false positivecompounds (i.e., apparently activating in this case) due toauto-fluorescence or quenching, but also correct the read-out for autofluorescent compounds. In a drug discovery process, this does not onlysave precious money and time, but also helps to find possible positiveand auto-fluorescent drug candidates which would otherwise be lost.

Example 5

In a further measurement series, the identification step was applied toa high-throughput-screening (HTS) run. In this HTS run differentcompounds were tested for the inhibition of the dephosphorylation of aphosphotyrosine-containing peptide by an appropriate protein tyrosinephosphatase. An antibody was used in this experiment which only binds tothe phosphorylated peptide. Since the peptide was fluorescently labeled,binding of the antibody to the phosphorylated peptide increased thepolarization, P, of this complex. Therefore, dephosphorylation resultedin a loss of antibody binding and concomitant decrease of polarization.A positive compound, which inhibited the dephosphorylation, shouldtherefore result in an increase of polarization, P. To check thereactivity of the compounds, the polarization read-out was compared tothat of positive and negative controls. While the negative control justlike a non-inhibiting compound comprised the dephosphorylated peptide(low polarization), the positive control just like an inhibitingcompound comprised the antibody-peptide complex (high polarization). Asin the previous examples 2 and 4, the measurements were performed withtwo detectors monitoring the different polarization directions of thelight emission and analyzed using 2D-FIDA with a one-component fit. Asoutlined, this enabled the calculation of the inhibition, Inh, as wellas the normalized total signal, NI.

6144 different compounds were added to the assay (labeled peptide,antibody, and phosphatase) and distributed on four differentnanotiter-plates with 2080 wells each. Furthermore, each plate contained24 positive and 24 negative control samples. The HTS run was performedby measuring each sample once for one second. The identification stepfor auto-fluorescent or quenching compounds is outlined in FIG. 5A. Inthe same way as outlined in detail in FIG. 2A and FIG. 4A, the thresholdconditions for the identification were set individually for each plateaccording to the mean values and standard deviations of Inh and NI ofthe positive and negative controls (mean +3×standard deviation).

This is shown in FIG. 5A for one of the four plates, where the thresholdlines (black lines) are drawn such as in FIGS. 2A and 4A. The compoundsamples exhibiting a read-out pair of (Inh,NI) above the upper line wereclassified as auto-fluorescent compounds (gray cross), while compoundsamples exhibiting a read-out pair of (Inh,NI) below the lower line wereclassified as quenching compounds (gray circles). Valid compound samplesas well as positive and negative controls (black circles) lie in betweenthe threshold lines. In this way, 1313 compounds were classified to bevalid, 166 (10.8%) to be quenching, and 57 (3.7%) to beauto-fluorescent.

FIG. 5B plots the pairs (Inh, NI) from all four plates. Theidentification step was performed for each plate independently. In thisway, 4966 valid (black circles), 819 quenching (13.3%, gray circles),and 365 auto-fluorescent compounds (5.9%, gray cross) were identified inthis HTS run.

Since the inhibition values obtained from the samples withauto-fluorescent and quenching compounds in a lot of cases pretend apositive inhibiting property of the according compound (compare FIG. 5),this identification step avoids the detection of false positives andhelps to save precious money and time in the drug discovery process whenusing HTS.

Example 6

FIG. 6 shows a schematic diagram of a preferred system for detecting theimpacts of interfering effects on experimental data resulting fromfluorescence measurements. Preferably, the fluorescence measurements areperformed with a confocal epi-illuminated microscope.

Means in an inspection station (2) support one or a plurality of samples(e.g. a moveable microscope table with a 4×6-, 96-, 384-, 1536-, or2080-well glass bottom well plate, the wells being filled with thesamples). Preferably, the samples comprise dye-labeled molecules at arather low concentration below 20 nM. Furthermore, the inspectionstation can preferably be moved with respect to the rest of the system.

One or a plurality of light sources (3) serve for the excitation offluorescence emission within the sample. Preferably, the light sourcesare linearly polarized lasers at wavelengths between 350 and 700 nm,which are either continuous wave or pulsed in the case of fluorescencelifetime measurements. In order to guide the excitation light onto thesample, it is reflected by a mirror (4) and focused into the sample by alens (5). Preferably, the mirror is dichroitic, i.e. it reflects theexcitation light and transmits the fluorescence light. Preferably, thelens is an objective lens, which focuses the light to an almostdiffraction limited spot of about 1 μm diameter, thereby causingfluorescence emission within the sample.

For the detection of the fluorescence emission, the system comprises anoptical set-up which directs the fluorescence on at least one of thedetectors (9, 10). The fluorescence of the sample is collected by thesame lens (5), transmits the mirror (4), and is focused onto a pinhole(6). The pinhole, which preferably has a diameter of 10 to 200 μm,blocks out-of-focus light and transmits only fluorescence light, whichis emitted within the focal part of the excitation light, i.e. a volumeof about fL-size. After the pinhole, the fluorescence is guided to oneor more detectors (9, 10). It can be split into several components byone or more mirrors (7), which preferably split the fluorescence intoits components of different polarization and/or color. Before impingingonto the detectors, the fluorescence radiation can pass optical filters(8), which preferably transmit the fluorescence and block unwantedradiation such as scattering from the solvent. Preferably, the detectors(9, 10) are avalanche photodiodes, which convert the impingedfluorescence radiation into an electrical signal with a very highefficiency.

A signal processing unit (11) converts the electrical signal of the oneor the plurality of detectors into experimental data, which ispreferably a stream of fluorescence photon counts. In further processingsteps, the unit (11) determines the values of one or a plurality ofidentification parameters from the experimental data, which is e.g. theamount of detected fluorescence—e.g. the fluorescence intensity, thenumber of counts and/or the count-rate-, a ratio of fluorescenceintensities at selected wavelengths, a ratio of fluorescence intensitiesat different polarization directions, a fluorescence anisotropy, afluorescence polarization, a fluorescence lifetime, a rotationalcorrelation time, a diffusion constant, a concentration of fluorophores,a specific fluorescence brightness, and/or a function of these. For thisdetermination, the signal processing unit uses preferably analysistechniques such as FCS, 1D- and/or 2D-FIDA, FILDA, fluorescence lifetimeand/or time-resolves anisotropy analysis, and/or FIMDA. Furthermore, thesignal processing unit (11) might coordinate the movement of the samplesupport within the inspection station. The signal processing unitpreferably contains a storage unit, which stores the determined valuesof identification parameters in relation to the respective position ofthe sample support. The signal processing unit (11) as well creates anhistogram or distribution of the values of the identification parametersand determines thresholds for the values of the identificationparameters, which thresholds are indicative for the impact ofinterfering effects. It analyzes the values of the identificationparameters for the different positions of the sample support within theinspection station and determines whether or not these values fulfillcriteria with respect to the thresholds. It also supplies as outputinformation those data which are influenced and/or not influenced by theinterfering effects. Furthermore, the unit (11) includes means forcorrecting the data for the impact of the interfering effect and meansfor the conductance of a control step to check the success of thecorrection.

1-44. (canceled) 45: A method for detecting the impacts of interferingeffects on data, such as experimental data, comprising the steps of: (i)providing the data, (ii) determining values of one or a plurality ofidentification parameters from said data, (iii) creating a histogram ordistribution of the values of the identification parameters, (iv)determining one or a plurality of thresholds for the values ofidentification parameters from said histogram or distribution, whichthresholds are indicative for the interfering effects, (v) analyzing thevalues of one or a plurality of identification parameters whether or notthese values fulfill one or a plurality of criteria with respect to thethresholds, and (vi) determining those data which are influenced and/orthose data which are not affected by the interfering effects.